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A real estate agent wishes to examine the relationship between the selling price of 2 home (measured in thousands of dollars) and its size (measured in square feet). In particular she wants to assess if the size of the home can accurately predict the house’s selling price. Use the sample data in the WS1-Houses spreadsheet to answer the following questions about the relationship. 1. Which characteristic is the dependent variable? Y = 2. Which characteristic is the independent variable? X = 3. Create a scatter plot for the data set. What does this tell you about the relationship between the variables? 4, What are the values of the regression coefficients? e bo= e b= 5. Write down the equation for the prediction line: e Y= e The value of a house increases on average by how much for each additional one square foot of size? e When the square footage of a house is 0 ft?, what is the average predicted selling price? 6. Use the prediction line to complete the following statements: e The Predicted Price for a house that is 2000 sq. feet is: e The Predicted Price for a house that is 2450 sq. feet is: e The Residual for a house that is 2450 sq. feet is: 7. What is the value of the Coefficient of Determination? e M= e Would you say the linear relationship between the variables is strong? Explain. 8. What is the value of the Standard Error of the Estimate? e O= SYX = e What does the standard error measure? 9. Is there sufficient evidence that the variables X and Y have a linear relationship? Use 2 0.1 level of significance. Herewearetestingl—():pl =0 vs l-( : B, 20 10. Based on all of the above questions, what will you conclude about the relationship between house size and selling price? 11. Construct a 95% confidence interval estimate of the population slope, B.. 12. Construct a 95% confidence estimate of the mean cost of a house that is 2000 square feet. 13. Construct a 95% prediction interval of the cost of a meal for an individual house that is 2000 square feet.